Which of the following describes the general relationship between genes and personality

J Pers. Author manuscript; available in PMC 2009 Dec 1.

Published in final edited form as:

PMCID: PMC2593100

NIHMSID: NIHMS75746

Wendy Johnson

University of Minnesota – Twin Cities and University of Edinburgh

Abstract

Twin studies of personality are consistent in attributing approximately half of the variance in personality to genetic effects, with the remaining variance attributed to environments that make people within the same families different. Such conclusions, however, are based on quantitative models of human individual differences that estimate genetic and environmental contributions as constants for entire populations. Recent advances in statistical modeling allow for the possibility of estimating genetic and environmental contributions contingent on other variables, allowing the quantification of phenomena that have traditionally been characterized as gene-environment interaction and correlation. We applied these newer models to understand how adolescents’ descriptions of their relationships with their parents might change or moderate the impact of genetic and environmental factors on personality. We documented notable moderation in the domains of positive and negative emotionality, with parental relationships acting to both enhance and diminish both genetic and environmental effects. We discuss how genetic and environmental contributions to personality might be more richly conceptualized as dynamic systems of gene-environment interplay – systems that are not captured by classical concepts, such as the overall heritability of personality.

Keywords: Twin study, Behavior Genetics, Interpersonal Relationships

Individual differences are heritable, by which we mean that genetic influences make a substantial contribution to individual differences in peoples’ observable characteristics [or “phenotypes”]. Indeed, this finding is so universal that Turkheimer [2000] enshrined it as the “first law” of behavior genetics. Turkheimer [2000] went on to propose second and third laws as well. The “second law” states that being raised in the same family has a smaller effect on individual differences than genetic effects. The “third law” is that a nontrivial portion of individual differences can be attributed to effects unique to each individual person, beyond genetic differences, and also beyond being raised in the same family.

These laws are well-supported by an extensive literature, and they clearly apply to personality as much as they apply to other individual differences [for recent reviews of the behavior genetics of personality, see, e.g., Krueger, Johnson & Kling, 2006; Krueger & Johnson, in press]. Indeed, these laws have had a fundamental impact on thinking in scientific psychology. Our late colleague David Lykken described the impact of behavior genetic studies as “rearranging the furniture in psychology's house.” The “furniture,” or the topics of interest in psychology, was not fundamentally changed by behavior genetic research. For example, the phenotypic, observable structure of personality [Goldberg, 1993] would be a topic of basic importance, regardless of the etiology of personality. Nevertheless, the “rearrangement” – understanding that the etiology of personality is partly genetic – requires one to think about personality in a different way from the “radical environmentalist” viewpoint that dominated much of psychology throughout its history [e.g., Watson, 1930]. A person's personality is not solely a product of environmental forces acting “from the outside”; personality is also the result of a genetic blueprint leading the person to actively seek out and interpret external environments in unique ways [Bouchard, 1997].

Given that we are at the point that scientific “laws” are being proposed based on an extensive literature [Turkheimer, 2000], have behavior genetic studies of personality and other individual differences outlived their usefulness? Our answer is “no,” and we arrived at this answer because the three behavior genetic laws strike us as vague, albeit of fundamental historical importance. The “laws” provided a needed corrective to the idea that people are puppets with strings pulled by the external environment. Nevertheless, the laws are derived from an approach to behavior genetic inquiry that estimates genetic and environmental effects on people in general. For example, when researchers conclude that “the heritability of extraversion is 50%,” they are concluding that 50% of the total variance in extraversion in their sample is associated with genetic influences. They can not conclude that a specific person's extraversion level is “50% genetic;” the concept of heritability applies not to individuals, but rather, to differences among many individuals. Stated in statistical terms, heritability applies to the variance of a set of observations, rather than to a single specific observation.

We can think about the meaning of heritability by drawing an analogy to the average score on a variable [the mean]. Consider estimating the mean level of aggression for a group of people that includes both men and women. If we compute the mean for the overall group, collapsing across gender, we will understand the typical level of aggression in the group. This estimate will be accurate and meaningful, but very general – it applies to people in general, as opposed to people with specific characteristics [e.g., men vs. women]. If we consider the possibility that gender affects [moderates] aggression, and examine mean aggression levels for men and women separately, we will get a more nuanced, less general understanding of aggression, finding that the mean for men is higher than the mean for women [Campbell, 2002]. It is not that the overall mean of aggression for human beings is somehow “inaccurate;” rather, a more nuanced understanding is achieved by considering how gender moderates aggression levels.

This is exactly the situation with behavior genetic inquiry into the etiology of human individual differences. The “three laws of behavior genetics” are not inaccurate, they are simply highly general, being based on estimates that apply to people in general, as opposed to persons with specific characteristics. Fortunately, recent developments in statistical methodology allow for more fine-grained estimates of the impact of genetic and environmental factors on individual differences. These developments go well beyond incorporating coarse, ordinal moderator variables [e.g., dichotomous moderator variables such as gender]. Newer models can also handle continuous moderator variables with theoretically compelling implications for understanding personality, such as peoples’ perceptions of their relationships with important others.

Before we delve into the nature of these modeling developments and apply them to understanding the etiology of personality, we first turn to describe the basic model for human individual differences that forms the foundations of research in behavior genetics. Some understanding of this basic model is needed to appreciate recent modeling refinements that allow us to go beyond the “three laws,” to achieve a more nuanced understanding of how genetic and environmental factors interact and correlate to produce individual differences in personality.

The ACE model of Individual Differences

Turkheimer's [2000] three laws are statements about the typically-observed values of three quantities that can be estimated from genetically-informative data, such as data on twins. These quantities are the effects of additive genetic factors [abbreviated A], environments that make people in the same families similar [“common” environments, abbreviated C], and environments that make people in the same families different [“non-shared” or “unique” environments, abbreviated E]. Genetically-informative data, such as data collected from twins, can be used to estimate A, C, and E [for a standard textbook account, see Neale & Cardon, 1992]. Stated in terms of a formal quantitative model for human individual differences,

In Equation [1], p2 represents the total observed [phenotypic, hence “p”] variance in a trait. This is the same variance statistic described in all standard introductory statistics textbooks, i.e., the average squared difference between the observed scores on a variable and the mean of that variable. The variance is an index of how “spread out” the scores are, or how different people tend to be from one another. As we described earlier, Turkheimer's [2000] three laws are actually statements about the typical values of a2, c2 and e2 found in behavior genetic research. For personality traits, a2 tends to be around 50% of p2, c2 tends to be around 0% of p2, and e2 tends to be around 50% of p2 [Bouchard & Loehlin, 2001]. Typical values of a2 are the basis for Turkheimer's first law [genetic effects are non-trivial for all individual differences], typical c2 values correspond with the second law [being raised in the same family has a smaller effect on individual differences than genetic effects], and e2 corresponds with the third law [effects unique to each individual person, beyond a2 and c2, are non-trivial].

The ACE model in Equation 1 is illustrated graphically as a path diagram in Figure 1, which portrays the ACE model as applied to data on identical [monozygotic, or MZ] and fraternal [dizygotic, or DZ] twin pairs. Following standard tracing rules for path diagrams [see, e.g., Loehlin, 2004], and standardizing the variances of the latent A, C, and E variables [the circles at the top of Figure 1] to 1.0, the diagram shows how the model in Figure 1 corresponds to Equation 1. This is shown by the paths pointing at phenotypes [labeled “Twin 1” and “Twin 2”] observed in the first and second twins in the pairs of twins in the study. The variance in those phenotypes is the sum of the ACE effects because, following the tracing rules for path diagrams, deriving the variances for Twin 1 and Twin 2 requires traversing up each path and back down to the observed phenotypes for each effect, multiplying paths involved in each individual effect, and summing the effects to get the total model predicted variances. The phenotypic variables have the same predicted variance because the paths pointing at them are set up to have the same coefficients [a, c, and e], so both have predicted variance = p2 [moreover, there is no reason that the Twin 1 and Twin 2 variables should differ in any way that would affect their variances]. Traversing the A path produces a [up to A] × a [back down to P] = a2, traversing the C path produces c2, traversing the E path produces e2, and the sum of the three effects is a2 + c2 + e2 = p2.

Standard model for the etiology of individual differences in a single observable characteristic measured in twin pairs. A= additive genetic variance, C=shared environmental variance, E=nonshared environmental variance.

Figure 1 also shows how twin data define the A, C, and E effects. These definitions are provided by curved lines at the top part of the Figure, which represent model-defined correlations between the A, C, and E effects, across halves of twin pairs. The A effect is defined by the distinct biological processes that produce MZ and DZ twins. MZ twins share the same genotype, and hence the correlation between the A effects across the 1st and 2nd twins in the MZ twins is 1.0. By comparison, DZ twins share, on average, half of the genes that differ from person to person. Accordingly, their A correlation is .5 [for a fascinating study that verifies the accuracy of the .5 value via modern molecular genetic methods, see Visscher et al., 2006]. Expressing a2 as a proportion of p2 gives the well-known heritability statistic, the extent to which observable, phenotypic individual differences are attributable to genetic differences.

Shared environmental [C] effects are defined by a correlation of 1.0 across the halves of the twin pairs for both MZ and DZ twins. This predicted 1.0 correlation derives from the definition of a shared environmental effect – the extent to which growing up in the same family makes people the same, independent of genetic similarity. If shared environmental effects are a major explanation for individual differences, then everyone growing up in the same family should turn out similar, regardless of their genetic similarity, and c2 should be a substantial proportion of p2.

Nonshared environmental [E] effects are defined by a correlation of 0.0 across the halves of the twin pairs, for both MZ and DZ twins. Like shared environmental effects, this predicted correlation derives from the definition of a nonshared environmental effect. This effect is the extent to which people are distinct [uncorrelated, or no more similar than two randomly paired people], in spite of sharing genetic material within families, and growing up together. If nonshared environmental effects are a major explanation for individual differences, then everyone should turn out relatively uniquely, regardless of their genetic similarity or the fact that some people grew up in the same families, and e2 should be a substantial proportion of p2. Nonshared environmental variance terms reflect the variance remaining after the effects of additive genetic and shared environmental variance have been estimated, and therefore random error variance is included in this variance component.

Expanding the ACE Model to Encompass Interaction and Correlation

The model in Figure 1 has been highly generative in personality research, forming the basis for numerous studies that yield a consistent picture of the etiology of personality as consisting of mostly genetic and nonshared environmental effects [Krueger & Johnson, in press]. Although fundamental, the model in Figure 1 also has some notable limitations, owing to its simplicity. First, the model is written for only a single phenotype, yet we live in a complex, multivariate world. Along these lines, the model defines the A, C and E effects for only a single variable. That is, the model lacks a way of handling correlations between genetic and environmental effects on multiple variables. Second, as described earlier, the model defines the effects of A, C, and E as independent and as applying equally to everyone in the sample. That is, the model lacks a way of handling moderation [a phenomenon that is also sometimes termed “gene-environment interaction”], as well as a way of handling genetically influenced exposure to different environments [gene-environment correlation].

Although these limitations are notable, the way forward is to develop more sophisticated models that overcome these limitations by trying to capture some additional complexities that might exist in nature, that are glossed over by the basic model in Figure 1. Such modeling developments form an active area of inquiry in behavior genetics [see e.g., Eaves & Erkanli, 2003; Johnson, 2007; Purcell, 2002]. The developments we will explore here involve defining the ACE model for two phenotypes, and in ways that allow the phenotypes in the model to transact, thereby providing a more nuanced understanding than can be achieved from evaluating only the a, c and e values from Figure 1.

In the model we will use, one phenotype acts to moderate the other phenotype, and the correlation between the phenotypes is also modeled directly. Simulation studies of this model were provided by Purcell [2002], some recent empirical applications of the model in personality research can be seen in Johnson and Krueger [2005, 2006], and Johnson [2007] describes the broad relevance of this model for providing a richer understanding of how genes and environments transact in human behavior.

The model is portrayed in Figure 2. For simplicity, the model in Figure 2 is drawn for one twin in each pair only; the A, C and E correlations across the two members of each pair, although not shown, are defined in exactly the same way as in Figure 1. Although Figure 2 may look daunting, it is a logical extension of the model in Figure 1, made possible by recent technical developments in model fitting. The key development allowing the model in Figure 2 to be fit to real data involves the ability to model the data obtained from all the persons in a study directly, as opposed to having to first summarize the data. Traditionally, the model in Figure 1 [and other statistical models] have been fit to summary data, such as the means, variances, and covariances of a set of variables. This is a perfectly reasonable thing to do if these summary data contain all the interesting information in a dataset. For example, the model in Figure 1 was traditionally fit to the within twin variances and cross-twin covariances of the single phenotype, estimated separately for MZ and DZ twin pairs. But summarizing the data in this manner means the ACE effects apply to people in general, and this is a limitation that would be hard to overcome in a summary statistics framework. Consider, for example, the possibility that the ACE values for a personality trait differ based on how adolescents perceive their relationships with their parents. If perceived parenting is assessed continuously and has, e.g., 25 distinct values, one would need to model 25 covariance matrices for both MZ and DZ twins [so 50 matrices total]! Clearly, this approach becomes unwieldy quickly. Fortunately, the problem can be circumvented by predicting not the summary statistics for the data, but the values of the raw data directly. Indeed, this kind of raw data modeling has opened up exciting conceptual possibilities in a number of areas of individual differences research [see, e.g., Krueger, Markon, Patrick, & Iacono, 2005, for an example of how raw data modeling can distinguish categorical and dimensional models of individual differences].

Model for the etiology of individual differences in a trait allowing for potential moderation. A= additive genetic variance, C=shared environmental variance, E=nonshared environmental variance.

The model in Figure 2 involves the same A, C, and E effects as in Figure 1, but also involves two fundamental extensions of Figure 1. First, the model now involves two phenotypes, as opposed to one phenotype in Figure 1, and it allows for both genetic and environmental connections between the phenotypes, via the ACE “common” effects [labeled Ac, Cc, and Ec on the Figure, the c suffix denoting effects in common]. Second, the model allows for the possibility that the level of the first phenotype [the Moderator, M] moderates the ACE components of the second phenotype [the Trait, T]. Some of these moderating effects are in common between M and T [those involving the c suffix, in the middle of the Figure], whereas others are unique to T [those involving the u suffix, to denote unique effects, on the right side of the Figure]. Each of the ACE pathways impacting T has the form “effect + [moderated effect × level of the moderator]”. That is, each of these paths contains an overall coefficient that is separate from the level of the moderator [e.g., for A effects in common between M and T, this is denoted as Ac], and a coefficient that indexes how much the effect is changed [moderated] by the level of the moderator [e.g., for A effects in common between M and T, this is denoted Amc, and it is multiplied by m because it represents how much the level of M changes the impact of the genetic effects on T that are in common between M and T].

As a result of this algebraic arrangement, each of the ACE effects on T is not static, but can vary dynamically as a function of the level of M. This allows for the possibility of “gene × environment interaction,” in the sense that the genetic and environmental aspects of T are not single static values estimated for the entire population, but rather, interact with M. In addition, the possibility of “gene-environment correlation” is handled in the Figure 2 model via the genetic and environmental connections between M and T. M and T are allowed to correlate, via both genetic [A] and environmental [C and E] pathways.

Personality and Relationships with Parents in Late Adolescence

The availability of these new biometrical moderation models allow us to test whether the heritability of personality may vary as a function of different environmental variables. Developmental psychologists have long acknowledged the role that family environment, particularly parenting style, parental monitoring, and the nature of the parent-child relationship, has in the development of temperament and personality [Thomas, Chess, Birch, Hertzig, & Korn, 1963]. While some have questioned the importance of parental influence on later psychological outcomes [Harris, 1995, 1998], recent reviews provide empirical evidence of the links between parenting and children's behavioral outcomes [Bates & McFadyen-Ketchum, 2000; Gallagher, 2002; Putnam, Sanson, & Rothbart, 2002]. Negative, inappropriate, uninvolved, or unskilled parenting variables appear to play a particularly important role in the development of externalizing behaviors, while warm and supportive parenting behaviors seem to act as protective factors [Bates, Petit, Dodge, & Ridge, 1998; Belsky, Hsieh, & Crnic, 1998; Rubin, Burgess, Dwyer, & Hastins, 2003; Stoolmiller, 2001]. Currently, there appears to be a growing consensus that parenting may influence adolescent development by acting as a moderator of other effects, such as genetic effects [Collins, Maccoby, Steinberg, Hetherington, & Bornstein, 2000]. Thus, we hypothesized that an adolescent's perception of parenting behaviors would moderate genetic influences on personality.

Specifically, we applied the model in Figure 2 to data on adolescents’ personality traits and their accounts of their relationships with their parents. The low C [shared environmental] values obtained for personality traits mean that people are not similar in their personalities within families, beyond the similarity predicted by knowing the genetic relationships among people within families. One interpretation of this finding is that parents do not have much impact on the personalities of their offspring [Harris, 1998]. This interpretation is reasonable if the only way in which parents impact offspring is by making them more similar to each other than they would be based on their genetic endowments. Another possibility is that the impact of parents is not an entirely shared environmental phenomenon. For example, parenting could act to make children within the same family different, in ways that are not correlated with genetic endowments. In this scenario, parenting is a nonshared environmental phenomenon, encompassed by the E component -- a component of variation that, for personality, is as large as the A component [Krueger & Johnson, in press].

Unfortunately, attempts to link the E variance in personality with specific measured psychological variables have not borne much fruit. Much research has sought to characterize E in psychological terms, particularly with regard to adolescent development, yet there has been little success in identifying systematic sources of E variation [Reiss, Neiderhiser, Hetherington, & Plomin, 2000]. Indeed, with regard to personality, some of E might actually be idiosyncratic, as opposed to being relevant to understanding consistent individual differences in behavioral propensities that transcend perspectives. For example, combining self and peer-reports of personality leads to larger estimates of A, and smaller estimates of E, when compared with modeling self and peer-reports of personality separately [Riemann et al., 1997].

In addition, parenting behaviors need not be purely environmental phenomena. A number of studies have shown that peoples’ reports of how they approach the task of parenting are partly heritable [Kendler, 1996; Losoya et al., 1997; Perusse, Neale, Heath, & Eaves, 1994]. Nevertheless, Spinath and O'Connor [2003] also showed how the personalities of parents and their reports of how they approach parenting are connected primarily through environmental mechanisms. This literature suggests a complex system of intertwining genetic and environmental influences on parenting [cf. McGue, Elkins, Walden, & Iacono, 2005].

In the current research, rather than thinking about parenting as linked to shared or nonshared environments, we conceptualized parenting as an emergent phenotype, with both genetic and environmental aspects. In addition, we assessed parenting from the perspective of the persons receiving the parenting, our adolescent twin research participants. That is, we asked our participants to independently describe their unique relationships with their parents. We treated these parental relationship measures as potential moderators of genetic and environmental effects on personality, and also as phenotypes that may be correlated with personality, through both genetic and environmental processes. Specifically, our adolescent twins’ reports of their relationships with their parents served as the “M” variable in the model in Figure 2, and their personality traits served as the “T” variable

Method

Research Participants

Our participants were male and female twin pairs who participated in the Minnesota Twin Family Study [MTFS], an ongoing population-based, longitudinal study of twins born in the state of Minnesota and their families. More than 90% of twin births from 1971 through 1985 were located from birth records and public databases. Families were excluded from the study if either twin had a cognitive or physical handicap that would prevent them from completing our daylong, in-person assessment, or if the family lived more than one day's drive from our laboratory at the University of Minnesota in Minneapolis. Of the eligible families, 83% agreed to participate. There were no significant differences between participating and nonparticipating families in socioeconomic status and self-reported mental health problems, but parents in participating families had slightly, albeit significantly, more education [0.25 years] than parents in nonparticipating families [Iacono, Carlson, Taylor, Elkins, & McGue, 1999]. Reflecting the population of Minnesota at the time of the twins’ birth, approximately 98% of the participants were Caucasian. Children gave informed assent, while parents gave informed consent for themselves and their children. The research protocol was approved by the University of Minnesota Institutional Review Board. Further information regarding all aspects of MTFS recruitment is detailed elsewhere [Iacono et al., 1999; Iacono, McGue, & Krueger, 2007].

The MTFS utilizes an accelerated longitudinal design, with twins first visiting the study at age 11 or 17 years and returning for follow-up assessments approximately every three years thereafter. The 11-year-old-intake cohort consisted of 756 same-sex, reared-together monozygotic [MZ] and dizygotic [DZ] twin pairs: 376 male [254 MZ; 122 DZ] and 380 female pairs [233 MZ; 147 DZ]. The 17-year-old-intake cohort consisted of 626 same-sex twin pairs: 289 male [188 MZ; 101 DZ] and 337 female [223 MZ; 114 DZ] pairs. For the purposes of the current study, we utilized data from both cohorts at the overlapping assessment point of age 17 years: the older cohort at intake and the younger cohort at their second follow-up visit. The sample thereby included all 1,252 individuals from the older cohort at the intake assessment, and 1,320 twins from the younger cohort who completed the second follow-up assessment [87% of the younger cohort]. From this total possible sample size of 2,572 individuals, participants were excluded if they were missing data on all of the personality variables and all of the parenting variables [N=155] and if co-twin data were entirely missing [N=97]. This brought the final sample size to 2,320 total persons who provided data on either personality or parenting. This sample included 556 male twin pairs [368 MZ; 188 DZ] and 604 female twin pairs [390 MZ; 214 DZ]. There were more MZ than DZ twins in the present sample as a result of an overrepresentation of MZ twins in the population from which the sample was drawn, as well as a somewhat higher participation rate of families with MZ twins [Hur, McGue, & Iacono, 1995]. Participants had a mean age of 17.76 [SD=.63] when they completed the measures used in the current project.

Zygosity Diagnosis

The MTFS combines three estimates to determine twin zygosity. First, parents complete a standard zygosity questionnaire. Second, MTFS staff evaluate physical similarity, including visage, hair color, and face and ear shape. Finally, ponderal and cephalic indices and fingerprint ridge count are measured. A previous validation study [N=50] demonstrated 100% accuracy of zygosity determination when these three estimates agree. When these three estimates do not agree, a blood sample is requested and a serological analysis is performed.

Assessment of Personality

Personality was measured with a shortened [198-item] version of the Multidimensional Personality Questionnaire [MPQ; Tellegen & Waller, in press] developed for the MTFS. The MPQ is a self-report personality instrument developed through factor analysis to assess a broad range of personality characteristics in normal populations. Internal consistency reliabilities for the MPQ scales range from .76 to .90, and 30-day test-retest reliabilities range from .82 to .92 [Tellegen & Waller, in press]. In the current research, we focused on three higher-order factors indexed by the MPQ: Positive Emotionality [PEM; a broad measure of positive well-being and tendency to view life as a pleasurable experience], Negative Emotionality [NEM; a propensity to experience psychological distress] and Constraint [CN; a tendency to endorse traditional values and act in a cautious manner]. Positive Emotionality subsumes the lower order scales of Well Being [cheerful, happy], Achievement [likes to work hard and strives for goals], Social Potency [likes to lead others], and Social Closeness [sociable, likes others]. Negative Emotionality is comprised of Aggression [hurts others for own advantage], Alienation [suspiciousness, thinks others intend harm], and Stress Reaction [proneness to negative emotions, tensions, and mood lability]. Finally, Constraint is a composite of Traditionalism [conservative, endorses high moral standards], Control [careful and planful], and Harm Avoidance [avoids danger in favor of safe activities].

All families were mailed the MPQ prior to the assessment. Participants were asked to bring the completed MPQ with them to their in-person visit. If the MPQ was not completed upon their arrival for their laboratory assessment or by the end of the day-long visit, participants were asked to complete it at home and return it by mail. MPQ data were available for 2,134 persons [men=1037, women=1097].

Relationship with Parents

The Parental Environment Questionnaire [PEQ] was administered to tap perceptions of mother-twin and father-twin relationships. The twins independently rated their relationships with each parent on 50 items assessing aspects of their relationship on a 4-point scale [1 = definitely true, 2 = probably true, 3 = probably false, and 4 = definitely false]. Because ratings about mom and dad were highly correlated [> .80; McGue et al., 2005], we combined these ratings.

Elkins, McGue, and Iacono [1997] provided a description of the development, theoretical rationale, and psychometric properties of the scale. Briefly, the PEQ was developed by the MTFS because the standard measures of family environment available when the study began typically sought to assess the overall family climate rather than dyadic relationships within the family. The PEQ scales were organized around the two broad domains of conflict [vs. nurturance/warmth] and control, and correlate significantly and in the expected direction with an alternative measure of the family environment [Elkins et al., 1997].

Scores were prorated for scales missing ratings for 10% or fewer of their items [i.e., the average of the other items was used as the missing item's score]; if more than 10% of the scale's constituent items were missing the scale was considered missing. For the present investigation, we utilized scores for the Parent-Child Conflict Scale [12 items: e.g., my parent often loses her/his temper with me; alpha = .82], the Regard for Parent Scale [8 items: e.g., I want to be like my parent in a number of ways; alpha = .75], and the Parent Regard for Twin Scale [5 items: e.g., my parent is proud of me; alpha = .69; McGue et al., 2005]. The correlation between the two Regard Scales was large [r = .65], so to simplify our analyses, scores for the two Regard Scales were combined to form one summary score, interpretable as mutual Regard.

All families were mailed the PEQ prior to the assessment. Participants were asked to bring the completed PEQ with them to their in-person visit. If the PEQ was not completed upon their arrival for their laboratory assessment or by the end of the day-long visit, participants were asked to complete it at home and return it by mail. One telephone prompt was made if a PEQ was still not received. PEQ data were available for 1,990 individuals for Conflict [men=939, women=1051] and 1,983 individuals for Regard [men=934, women=1049]. Although the Regard and Conflict scales were negatively correlated [r = −.66], we analyzed them separately because we wanted to consider the possibility that they would have different moderating effects.

Biometrical Analytic Approach

Statistical models for twin data [biometrical models] were fit to raw MPQ and PEQ data using the software package Mx [Neale, Boker, Xie, & Maes, 2002]. Because some MPQ and PEQ data were missing on some participants, we used full-information maximum-likelihood raw data techniques. When data are missing, this approach uses all available information to impute a value and then adjusts for the imprecision of the imputed value. The fit of the model shown in Figure 2 was judged relative to the fit of a model in which the moderation parameters were fixed at zero. That is, the parameters Amc, Cmc, Emc, Amu, Cmu, and Emu were fixed at zero, so that the paths contributing to the variance in the Trait become constants for the entire sample. For example, the unique A effect on the trait becomes Au + [0 × m] = Au, and the contribution of the Au effect to the variance in the trait is no longer affected by the level of the Moderator [m is multiplied by zero]. Comparing the fit of the two models [with and without moderator coefficients] thereby produces a statistical test of moderation. If the model with the moderation coefficients [Amc, Cmc, Emc, Amu, Cmu, and Emu] fits better than the model with those coefficients fixed at zero, then the ACE contributions to the Trait are not constant across levels of M, and moderation effects are important in understanding the etiology of the Trait.

Two indices were used to evaluate model fit: [1] the likelihood-ratio test [LRT; distributed as χ2, and computed as the difference in the −2 log-likelihood values for the two models]; and [2] Akaike's Information Criterion [AIC; Akaike, 1987]. Improvements in the model's fit, from adding or omitting parameters, can be assessed by noting the statistical significance of the LRT. Like the LRT, the AIC considers goodness of fit in the likelihood sense [how well the model reproduces the observed data], but also penalizes model complexity, preferring models that capture the data both accurately and parsimoniously. Lower AIC values are associated with better fitting models.

In the interests of focusing our analyses specifically on personality – parenting associations, all measured phenotypes were adjusted to be independent of age and sex effects [i.e., age, age2, age × gender, and age2 × gender were regressed out of the variables, and the standardized residuals from these regressions were used in our analyses; McGue & Bouchard, 1984]. In theory, the other option here would be to model age and gender, but this creates analytic complexities that are difficult to render tractable in practice [e.g., multi-way interactions that cannot be modeled readily], and that are also tangential to our current aims. In addition, to promote clarity in interpreting effects, the personality variables were recoded as necessary so that they would be positively correlated with the parenting variables, which remained scored in the same direction for all models, with higher levels referring to greater conflict or greater regard [e.g., our raw PEM variable was negatively correlated with Conflict, so it was reversed so the correlation would be positive].

Results

Phenotypic Correlations

We first sought to understand how personality traits are associated with perceived relationships with parents. We did this by estimating the observed, phenotypic associations between the parental relationship variables and the personality variables. Specifically, we estimated correlations using the MLR estimator in the software package M+ [Muthén & Muthén, 1998-2006], because by using this estimator, the confidence intervals around the correlations are adjusted for the non-independence of twins within pairs. Conflict was significantly correlated with PEM [r =− .14, 95% CI = −.09, −.20], NEM [r = .41, 95% CI= .37, .46], and CN [r = −.27, 95% CI=−.22, −.32]. Similarly, Regard was significantly correlated with PEM [r = 0.29, 95% CI=.23, .35], NEM [r = −.28, 95% CI=−.23, −.32], and CN [r = .24, 95% CI=.19, .29]. As these correlations show, PEM and CN were associated with more positive perceived relationships with parents, whereas NEM was associated with more negative perceived relationships with parents.

Biometric Analyses

We next sought to understand how these phenotypic relationships between personality and relationships with parents are mediated genetically and environmentally. We did this by fitting the model in Figure 2 to each combination of the two relationship variables [treated as M variables] and the three personality variables [treated as T variables], for a total of six models where the moderation parameters were freely estimated. We also fit the six models to the same variables, fixing the moderating effects to zero [i.e., the parameters Amc, Cmc, Emc, Amu, Cmu, and Emu were set to zero]. Comparing the fit of the pairs of models [with and without moderation] provides an evaluation of whether the moderation effects improve the ability of the model to capture our data. Table 1 gives the fit indices for these 12 models [6 with moderation, 6 without moderation].

Table 1

Fit Statistics from Biometrical Models with and without Moderation Coefficients

Note. −2lnL = −2 log likelihood; AIC = Akaike's Information Criterion; χ2 and associated p values correspond to the 6 df likelihood-ratio test comparing the −2lnL values for No Moderation and Moderation models. The selected model is shown in bold.

Table 1 shows that full moderation effects were notable for five of the six possible combinations of personality and parent relationship variables, according to both the LRT and AIC. The strongest were the moderating effects of Regard on PEM [χ2=45.82, df=6, p environment effects. Child Development. 1983;54:424–435. [PubMed] [Google Scholar]